Balancing Workload and Workforce Capacity in Lean Management: Application to Multi-Model Assembly Lines

Jordi Fortuny-Santos,Lluís Cuatrecasas-Arbós,Jordi Fortuny-Profitós,Patxi Ruiz-De-Arbulo-López

doi:10.3390/app10248829

Jordi Fortuny-Santos, Lluís Cuatrecasas-Arbós + Show 2 more

Open Access

https://doi.org/10.3390/app10248829

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Abstract

While multi-model assembly lines are used by advanced lean companies because of their flexibility (different models of a product are produced in small lots and reach the customers in a short lead time), most of the extant literature on how to staff assembly lines focuses either on single-model lines or on mixed-model lines. The literature on multi-model lines is scarce and results given by current methods may be of limited applicability. In consequence, we develop a procedure to staff multi-model assembly lines while taking into account the principles of lean manufacturing. As a first approach, we replace the concepts of operation time and desired cycle time by their reciprocal magnitudes workload and capacity, and we define the dimensionless term of unit workload (load/capacity ratio) in order to avoid magnitudes related to time such as cycle time because, in practice, they might not be known. Next, we develop the necessary equations to apply this framework to a multi-model line. Finally, a piece of software in Python is developed, taking advantage of Google’s OR-Tools solver, to achieve an optimal multi-model line with a constant workforce and with each workstation performing the same tasks across all models. Several instances are tested to ensure the performance of this method.

Highlights

Designing efficient assembly processes requires computing the number of people necessary to accomplish the task and assigning work elements to operators in an even manner
Since the cycle time of a serial line process equals the longest cycle time of the workstations [12], the longest station time of the assembly line cannot be above the takt time [39] or desired cycle time
In order to find an optimal solution with the minimum common number of workstations where the same tasks are assigned to the same workstations across the different models, as much as possible, while avoiding additional trial and error work, we developed a piece of software coded in Python

Summary

Introduction

Designing efficient assembly processes requires computing the number of people necessary to accomplish the task and assigning work elements to operators in an even manner. This is especially important in a lean management (LM) context, which requires products to be completed in a just-in-time fashion, without muda (Japanese for wasted resources in non-value-adding work), muri (people overburden) or mura (workload variation) [1]. “Lean” was the word used by researchers at the Massachusetts Institute of Technology (MIT), in the 1980s, to describe the efficient plants of Japanese car manufacturers [2].

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